Various organizations (such as business organizations, governments, etc.) have access to a rich variety of data concerning the actions and/or characteristics of entities. As used herein, an entity is typically a natural person but may also include collections of people or other organizations that are treated as a single unit (e.g., a family, small business, etc.). For such organizations, it is beneficial to be able to analyze such data (when legally permissible to do so) in order to determine the likelihood that a given entity or segment of entities is likely to engage in a certain behavior. As used herein, a behavior, i.e., a way that a given entity has or will behave, may be considered desirable (and, therefore, to be encouraged) or undesirable (and, therefore, to be discouraged) from the point of view of the organization performing the analysis.
For example, the strength of certain companies, such as subscriber-based service providers, are assessed by the rate of “churn”, i.e., the rate at which subscribing entities end the provider-user relationship. Such companies are keen to identify entities having a likelihood of engaging in “churn” behavior before they do so, thereby providing these companies the opportunity to prevent this occurrence. On the other hand, companies often want to encourage other behaviors, e.g., subscription to new or ancillary services, with those entities likely to be receptive to such encouragement. Of course, the same concept may be equally applied to other organizations and those behaviors, whether “positive” or “negative”, particularly applicable thereto.
Despite this need, many organizations lack the capabilities to perform the sophisticated analyses often used for this purpose. Even where such analytical resources are available, the lead time required to conduct such analyses often results in a loss of timing and relevancy, i.e., the window of time for an organization to identify and react to those entities likely to engage in a behavior may expire quickly.